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This course offers an introduction to optimization models and their applications, ranging from machine learning and statistics to decision-making and control, with emphasis on numerically tractable problems, such as linear or constrained least-squares optimization. The course covers two main topics: practical linear algebra and convex optimization.

The image on the left shows a graph of the Senators in the 2004-2006 US Senate, that is obtained by solving a specific optimization problem involving the estimation of covariance matrices with sparsity constraints. (For more details, see here.)

Syllabus: here.

Schedule: here.

To communicate: We do not use this site to communicate, post homeworks, etc. We use bCourses, and Piazza for student-GSI discussions. You can also send an email to‚Äč.

Link to UC Berkeley Schedule of classes: EECS 127 and EECS 227AT.


  1. 9/3/19: Quiz.

  2. 10/3/19: Midterm 1.

  3. 11/12/19: Midterm 2.

  4. 12/18/19, 8-11am: Final.